An evaluation of human act generation performance

Abstract

Act generation is important in decision making in ill-defined problems where the subject must synthesize actions that might solve the problem. Two experiments explored human abilities to generate actions which might solve ill-defined decision problems. Subjects were given unlimited time to suggest as many solutions as possible. Their suggestions were compared to a hierarchical structural model of the actions developed by the experimenters that could be taken to solve the problem. Although subjects were capable of generating several actions that might be worth taking, their suggestions were far from complete. The second experiment replicated and extended these results by introducing instructions to generate quality actions. It employed a structural model derived from cluster analysis, an improved utility estimation technique, and offered substantial monetary incentives for quality or quantity of actions generated. This study confirmed the general conclusion that subjects fail to generate important high-utility actions. The implications of this result are discussed in respect to decision analysis in which a complete structural model of the decision problem is highly desirable.